{
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  "Title": "Mixed-Frequency GARCH Models",
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  "Authors@R": "c(\nperson(\"Onno\", \"Kleen\", email = \"r@onnokleen.de\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4731-4640\"))\n)",
  "Description": "Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models\n(Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and\nrelated statistical inference, accompanying the paper \"Two are\nbetter than one: Volatility forecasting using multiplicative\ncomponent GARCH models\" by Conrad and Kleen (2020,\n<doi:10.1002/jae.2742>). The GARCH-MIDAS model decomposes the\nconditional variance of (daily) stock returns into a short- and\nlong-term component, where the latter may depend on an\nexogenous covariate sampled at a lower frequency.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
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  "URL": "https://github.com/onnokleen/mfGARCH/",
  "BugReports": "https://github.com/onnokleen/mfGARCH/issues",
  "Repository": "https://onnokleen.r-universe.dev",
  "Date/Publication": "2026-01-13 14:43:46 UTC",
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  "Maintainer": "Onno Kleen <r@onnokleen.de>",
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    "extra/citation.json",
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    "extra/readme.md",
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    "simulate_mfgarch",
    "simulate_mfgarch_diffusion",
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        "tbl",
        "data.frame"
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      ]
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      "page": "df_mfgarch",
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        "df_mfgarch"
      ]
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      "title": "This function estimates a multiplicative mixed-frequency GARCH model. For the sake of numerical stability, it is best to multiply log returns by 100.",
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        "fit_mfgarch"
      ]
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